chapter 15, Choosing a Lossy Compression Technique

In IV Lossy Compression Techniques from: Digital Image Compression Techniques

Author(s): Majid Rabbani, Paul W. Jones

Published: 01 February 1991

Chapter DOI: 10.1117/3.34917.ch15

Chapter Page Count: 7 pages

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Front Matter

I Background
1. Digital Images and Image Compression

References to Part 1

II Information Theory Concepts
2. Source Models and Entropy

3. Variable-Length Codes

4. Entropy Estimation and Lossless Compression

5. Rate-Distortion Theory and Lossy Compression

References to Part 2

III Lossless Compression Techniques
6. Bit Plane Encoding

7. Lossless Predictive Coding

8. Lossy Plus Lossless Residual Encoding

References to Part 3

IV Lossy Compression Techniques
9. Lossy Predictive Coding

10. Transform Coding

11. Block Truncation Coding

12. Vector Quantization

13. Subband Coding

14. Hierarchical Coding

15. Choosing a Lossy Compression Technique

References to Part 4

A. Compression of Color Images

Back Matter

Preview

Chapter Contents

15.1 Bit Rate/Quality Performance Summary

Excerpt

The question “What is the best lossy compression algorithm?” is often asked, but unfortunately there is no absolute answer. The choice of a particular algorithm for a given application depends on many factors. For example, when compression is used in an image transmission application, the encoding and decoding operation often needs to be performed in real time, and the issues of implementation complexity, susceptibility to channel errors, and buffering requirements to match the coder output rate to the transmission rate of the channel become important. In contrast, in applications where compression is used to reduce storage requirements, the encoding operation often does not need to be performed in real time. The encoder can be quite complex since it will be used only once for a given image, while a simple decoder is desirable since it will be used repeatedly. Also, the error rates encountered in storage and retrieval applications are typically many orders of magnitude lower than the error rates for a communications channel. This may allow for a more sophisticated algorithm with greater emphasis placed on final image quality.

The following is a list of factors that can influence the choice of a compression algorithm. In general, the weighting of each factor in making a decision is highly dependent on the application. This list is by no means exhaustive, and is intended to serve only as a general guide.

• Sensitivity to input image types: Since we are focusing on compression algorithms for continuous-tone imagery, we will not discuss the robustness of these techniques for text or computer-generated imagery.

DOI: 10.1117/3.34917.ch15

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